#data intelligence platform

[slingshotsimulations]

Slingshot Simulations is a data Intelligence platform and analytics built to put AI in the hands of commercial leaders, to drive great decision making, all the time. Contact our dedicated team - Complete our contact form and a member of our team will be in touch shortly.

[blackknight-100]

Today I found out that apparently, even LinkedIn, a professional platform expected to contain people's personal information, is using our data to train generative AI models, and one has to specifically opt-out if they don't want their data to be harvested by AI. This change is effective from November 20th in all regions except the European Economic Area, Switzerland, and the United Kingdom.

If you don't want your data to be used to train AI models, consider opting out by going to Settings -> Data Privacy -> Data for Generative AI improvement -> Toggle switch to "Off"

[acuvate-updates]

Revolutionizing Enterprises: CXO’s GenAI Transformation

1. Unlocking AI’s Potential: A Strategic Overview

AI adoption, embraced by 70% of executives, promises enhanced customer experiences despite challenges. Understanding and integrating AI into business operations is essential. Explore our guide for actionable insights, ensuring businesses not only survive but thrive in the AI-driven era.

Learn more about Artificial Intelligence impact in 2025

AI Reshaping Decision-Making in 2025

Generative AI, like GPT, simplifies business processes. It transforms decision-making with its user-friendly interfaces, self-learning capabilities, and efficient sorting.

Furthermore, it’s a budget-friendly solution with no training fees, making it accessible for businesses of various sizes.

Our guide aims to offer practical insights for responsibly adopting this transformative technology. Following our roadmap allows businesses to navigate the Generative AI landscape, ensuring success in the constantly changing digital environment.

To stay informed and up to date with the latest trends, join our webinars featuring industry experts from organizations like Microsoft, Shell, and more.

C-Suite Roles Transformed by AI

Strategic AI Adoption Tips for Leaders

To successfully adopt AI, prioritize it for strategic goals, use tailored features, and embrace multilingual capabilities. Ensure secure deployment for data integrity. Offices that adopt AI enjoy streamlined processes, ongoing innovation, and secure frameworks.

2. Transforming C-Suite Roles with AI

Empowering CIOs: Innovating IT with AI

In enterprise IT, AI, particularly models like GPT, empowers CIOs to break traditional boundaries and improve operations through groundbreaking innovations.

Use Cases:

· Smart IT Helpdesk Support: AI ensures 24x7 support with human-like conversations, reducing user effort and cost.

· Smart Search: AI transforms data management, improving user engagement with easy-to-use search capabilities.

· Next-Gen Customer Support: AI automates email-based queries, crafting personalized responses for enhanced customer experiences.

To stay informed and up to date with the latest trends, join our webinars featuring industry experts from organizations like Microsoft, Shell, and more.

Implementation Tips:

· Prioritize AI for strategic goals.

· Personalized and multilingual capabilities.

· Ensure secure deployment for data integrity.

· Offices embracing AI experience streamlined helpdesks, continuous innovation, and secure frameworks.

Empowering HR with AI: From Administration to Leadership

Use Cases:

· AI-powered Talent Acquisition: AI streamlines global recruitment, automating candidate screening and optimizing interview scheduling.

· Efficient Employee Onboarding: AI redefines onboarding by using chatbots to create personalized experiences and promote communication across departments.

· Personalized Employee Engagement: AI’s learning capabilities drive adaptive engagement activities, ensuring timely interventions and integrating feedback loops.

· Data-Driven Learning and Development: AI changes learning through advanced knowledge mining, personalized modules, and interactive interfaces.

Implementation Tips:

· Align AI integration with strategic HR goals.

· Leverage AI’s personalization and multilingual features.

· Uphold data integrity and fortify security during deployment.

· Offices leveraging AI experience streamlined recruitment, efficient onboarding, personalized engagement, and reimagined L&D.

Also, read more about How GPT-powered Chatbots Can Help HR Leaders Drive Engagement and Retention

AI-Powered Marketing: A CMO’s Secret Weapon

Use Cases:

· AI-Powered Brand Engagement Solutions: AI revolutionizes brand engagement with personalized content, human-like communication, and timely identification of upsell opportunities.

· Smartly allocate ad spending: AI enables CMOs to allocate budgets wisely by analyzing real-time market trends predictively.

Implementation Tips:

· Prioritize AI Integration aligned with core marketing goals.

· Leverage Multilingual Features for global brand reach.

· Strategize Deployment with a focus on data integrity and customer privacy.

· Offices with AI experience tailored brand engagement, proactive ad spend decisions, and seamless multilingual marketing.

Explore the Power of Generative AI for enhancing CX — Marketing and Customer support/ Engagement

AI: The COO’s Catalyst for Operational Agility and Efficiency

In the realm of Operational efficiency, Chief Operating Officers (COOs) orchestrate processes to optimize resources.

Use Cases:

· Simplifying the supply chain: Artificial Intelligence (AI) provides a high-level perspective, facilitating proactive demand forecasting and prompt corrective actions for effective supply chains.

· Enhancing Operational Communication: AI-powered chatbots ensure role-specific information flow, facilitating real-time feedback and swift issue resolution.

· Driving Operational Cost Optimization: AI analyzes data for cost leakage points, recommends resource redistribution, and encourages real-time cost insights.

To stay informed and up to date with the latest trends, join our webinars featuring industry experts from organizations like Microsoft, Shell, and more.

Implementation Tips:

· Justify Integration Effort with improved operational KPIs.

· Leverage Iterative Learning for continuous process refinement.

· Prioritize Data Security, safeguarding organizational assets.

· Offices with AI experience data-driven supply insights, intelligent communication, and dynamic cost optimization.

· In the dynamic field of data management, Chief Data Officers (CDOs) use AI, including GPT and other generative AI models, as strong supporters to decode large datasets effectively.

Use Cases:

· Enhancing Data Intelligence: AI’s advanced algorithms mine data, providing insights that shape business strategies through predictive modeling and intelligent summarizing.

· Managing Unstructured Data: AI’s NLP features efficiently process and convert unstructured data into organized, clear formats, enhancing data processing efficiency.

· Enhancing Data Governance: AI simplifies data management by automating organization, ensuring compliance with regulatory policies, real-time breach detection, and maintaining data standards.

Implementation Tips:

· Start with a clear data strategy aligning AI’s abilities with major data challenges.

· Prioritize data protection in AI adoption for utility and security.

· Invest in continuous training, refining AI models for better understanding of organizational data.

· Offices with AI experience automated, intelligent data insights, streamlined data, and proactive, AI-assisted data governance.

3. AI’s Impact: Boosting Enterprise Efficiency

Discover how advanced AI, including Azure OpenAI’s GPT, is reshaping enterprise operations. Explore real-world use cases across departments, showcasing the profound impact of Generative AI on organizational efficiency.

AI Integration Across Departments

SharePoint Search Integration

Structured Data Insights & Summarization

AI enables the effortless transformation of structured data into actionable intelligence. This module analyzes tables and databases, extracting meaningful insights presented in user-friendly natural language summaries, empowering teams for informed decision-making.

R&D Assistant

In Research and Development, AI acts as a dedicated assistant, leveraging internal and external data sources for comprehensive reports and analysis.

Customer/Consumer Support

Elevate customer support with an AI-powered chatbot that delivers personalized and context-aware responses. By training the model with customer support data, this solution ensures accuracy and seamless integration with existing systems.

HR Chatbot

AI becomes an invaluable virtual assistant in HR, guiding employees through common queries with personalized responses. From leave requests to company policies, this intelligent chatbot ensures a seamless and efficient employee experience.

IT Chatbot

Revolutionize IT support by using an AI-powered chatbot. The chatbot can troubleshoot common issues, give step-by-step instructions, and escalate complex cases. Enhance user experience and streamline technical support with this essential tool.

To stay informed and up to date with the latest trends, join our webinars featuring industry experts from organizations like Microsoft, Shell, and more.

Document Comparison/RFP Validation

AI streamlines procurement and HR processes by comparing documents. Quickly analyze text documents for similarities, differences, and changes, ensuring accuracy in document validation and specifications.

Procurement Assistant

Automate and streamline the procurement process with an AI-powered assistant. Generate purchase orders, request for quotations, and vendor evaluations based on predefined templates and user inputs, ensuring efficiency and accuracy.

Search Integration with SAP JAM/ServiceNow KB/Salesforce KB

Bridge the knowledge gap by integrating AI with ERP and ITSM systems. Enable interactive conversations beyond search results, enhancing user understanding and engagement with content.

Knowledge Management Solution

Empower your workforce with a Knowledge Management Solution seamlessly merging AI with Azure Cognitive Search. Unlock information from diverse sources, fostering a culture of knowledge-sharing and collaboration.

Integrate innovative AI use cases into your strategy for streamlined processes and enhanced user experiences.

4. Unlocking AI’s Power with Acuvate: A Comprehensive Guide

As businesses embrace AI’s transformative potential, Generative Pre-trained Transformers (GPT) take center stage, enhancing productivity. Our guide delves into AI FAQs, ensuring data security and adaptability for enterprise needs.

To stay informed and up to date with the latest trends, join our webinars featuring industry experts from organizations like Microsoft, Shell, and more.

Acuvate Advantage

Experience the Org Brain GPT framework, combining analytics and enterprise security. Acuvate’s expertise, spanning 16 years, ensures customized AI solutions for streamlined processes.

Explore our AI trends guide to boost your organization’s capabilities. Request a demo or insight into Acuvate’s transformative AI solutions for enhanced performance.

Also, read our other blogs on the AI revolution on Medium

9 Must-Watch Webinars of 2025 for Tech Enthusiasts | Medium

- AI-Driven Transformation: A CXO's Guide to Generative AI Success | Medium

GPT Revolution in AI - A Strategic Guide for CXO | Medium

Emerging Energy Technologies: Data, AI & Digital Solutions in 2025 | Medium

[shadowed-yet-vibrant]

I’m too tired and too busy to make a cogent post about my thoughts… but I’ve been getting more and more bothered by the onslaught of anti-AI posts on Tumblr. This website appears to collectively jump on any new tool being rolled out that’s “AI” in any fashion. And it’s such a disappointing, “change-is-scary” hive mind reaction.

AI is a broad, almost all-encompassing term for a wide range of certain technological capabilities. It’s everything from mimicking human language, to making more effective search engines, to detecting patterns in data. AI has been used in widely available tools and multiple professions for some time, and is just now becoming more visible with recent controversies (particularly in art and creative spaces).

Please stop frothing at the mouth when you see those two letters. While there are definitely areas where this response may be warranted after consideration and understanding its mechanics (as in creative arts), AI is not a default “evil.” Not all AI is made by scraping people’s online art/writing/conversations.

AI has the potential to help advance many fields and make our lives better and more efficient in little ways. Understanding where that is - as opposed to areas where AI will hurt artists and creators - requires critical thought. And critical thought means taking a deep breath and understanding what’s behind the curtain of the “AI buzzword.”

[datapeakbyfactr]

How to Choose the Best AI Tool for Your Data Workflow

AI isn’t just changing the way we work with data, it’s opening doors to entirely new possibilities. From streamlining everyday tasks to uncovering insights that were once out of reach, the right AI tools can make your data workflow smarter and more efficient. But with so many options out there, finding the one that fits can feel like searching for a needle in a haystack. That’s why taking the time to understand your needs and explore your options isn’t just smart, it’s essential. 

In this guide, we’ll walk you through a proven, easy-to-remember decision-making framework: The D.A.T.A. Method: a 4-step process to help you confidently choose the AI tool that fits your workflow, team, and goals. 

The D.A.T.A. Method: A Framework for Choosing AI Tools 

The D.A.T.A. Method stands for: 

Define your goals 

Analyze your data needs 

Test tools with real scenarios 

Assess scalability and fit 

Each step provides clarity and focus, helping you navigate a crowded market of AI platforms with confidence. 

Step 1: Define Your Goals 

Start by identifying the core problem you’re trying to solve. Without a clear purpose, it’s easy to be distracted by tools with impressive features but limited practical value for your needs. 

Ask yourself: 

What are you hoping to achieve with AI? 

Are you focused on automating workflows, building predictive models, generating insights, or something else? 

Who are the primary users: data scientists, analysts, or business stakeholders? 

What decisions or processes will this tool support? 

Having a well-defined objective will help narrow down your choices and align tool functionality with business impact. 

Step 2: Analyze Your Data Needs 

Different AI tools are designed for different types of data and use cases. Understanding the nature of your data is essential before selecting a platform. 

Consider the following: 

What types of data are you working with? (Structured, unstructured, text, image, time-series, etc.) 

How is your data stored? (Cloud databases, spreadsheets, APIs, third-party platforms) 

What is the size and volume of your data? 

Do you need real-time processing capabilities, or is batch processing sufficient? 

How clean or messy is your data? 

For example, if you're analyzing large volumes of unstructured text data, an NLP-focused platform like MonkeyLearn or Hugging Face may be more appropriate than a traditional BI tool. 

Step 3: Test Tools with Real Scenarios 

Don’t rely solely on vendor claims or product demos. The best way to evaluate an AI tool is by putting it to work in your own environment. 

Here’s how: 

Use a free trial, sandbox environment, or open-source version of the tool. 

Load a representative sample of your data. 

Attempt a key task that reflects a typical use case in your workflow. 

Assess the output, usability, and speed. 

During testing, ask: 

Is the setup process straightforward? 

How intuitive is the user interface? 

Can the tool deliver accurate, actionable results? 

How easy is it to collaborate and share results? 

This step ensures you're not just selecting a powerful tool, but one that your team can adopt and scale with minimal friction. 

Step 4: Assess Scalability and Fit 

Choosing a tool that meets your current needs is important, but so is planning for future growth. Consider how well a tool will scale with your team and data volume over time. 

Evaluate: 

Scalability: Can it handle larger datasets, more complex models, or multiple users? 

Integration: Does it connect easily with your existing tech stack and data pipelines? 

Collaboration: Can teams work together within the platform effectively? 

Support: Is there a responsive support team, active user community, and comprehensive documentation? 

Cost: Does the pricing model align with your budget and usage patterns? 

A well-fitting AI tool should enhance (not hinder) your existing workflow and strategic roadmap. 

“The best tools are the ones that solve real problems, not just the ones with the shiniest features.”

— Ben Lorica (Data scientist and AI conference organizer)

Categories of AI Tools to Explore 

To help narrow your search, here’s an overview of AI tool categories commonly used in data workflows: 

Data Preparation and Cleaning 

Trifacta 

Alteryx 

DataRobot 

Machine Learning Platforms 

Google Cloud AI Platform 

Azure ML Studio 

H2O.ai 

Business Intelligence and Visualization 

Tableau – Enterprise-grade dashboards and visual analytics. 

Power BI – Microsoft’s comprehensive business analytics suite. 

ThoughtSpot – Search-driven analytics and natural language querying. 

DataPeak by Factr – A next-generation AI assistant that’s ideal for teams looking to enhance decision-making with minimal manual querying.  

AI Automation and Workflow Tools 

UiPath 

Automation Anywhere 

Zapier (AI integrations) 

Data Integration and ETL 

Talend 

Fivetran 

Apache NiFi 

Use the D.A.T.A. Method to determine which combination of these tools best supports your goals, data structure, and team workflows. 

AI Tool Selection Checklist 

Here’s a practical checklist to guide your evaluation process: 

Have you clearly defined your use case and goals? 

Do you understand your data’s structure, source, and quality? 

Have you tested the tool with a real-world task? 

Can the tool scale with your team and data needs? 

Is the pricing model sustainable and aligned with your usage? 

Does it integrate smoothly into your existing workflow? 

Is support readily available? 

Selecting the right AI tool is not about chasing the newest technology, it’s about aligning the tool with your specific needs, goals, and data ecosystem. The D.A.T.A. Method offers a simple, repeatable way to bring structure and strategy to your decision-making process. 

With a thoughtful approach, you can cut through the noise, avoid common pitfalls, and choose a solution that genuinely enhances your workflow. The perfect AI tool isn’t the one with the most features, it’s the one that fits your needs today and grows with you tomorrow.

Learn more about DataPeak:

[lumenore-datalytics]

How AI-Powered Analytics Is Transforming Healthcare in 2025

In healthcare, seconds save lives. Imagine AI predicting a heart attack hours before symptoms strike or detecting cancer from a routine scan. This isn’t science fiction—AI-powered analytics in healthcare is making this a reality, turning data into life-saving insights. 

By analyzing vast amounts of data, AI healthcare analytics help decode hidden patterns, improving diagnoses and personalizing treatments, which were unimaginable until a few years ago. The global healthcare analytics market is projected to hit $167 billion by 2030, growing at a 21.1% CAGR, thereby proving that data is becoming the foundation of modern medicine. 

From real-time analytics in healthcare to AI-driven insights, the industry is witnessing a revolution—one that enhances patient care, optimizes hospital operations, and accelerates drug discovery. The future of healthcare is smarter, faster, and data-driven. 

What Is AI-Powered Analytics in Healthcare?

AI-powered analytics uses artificial intelligence and machine learning to analyze patient data, detect patterns, and predict health risks. This empowers healthcare providers to make smarter, faster, and more personalized decisions. Here’s how this data revolution is reshaping healthcare:

1. Early Diagnosis and Predictive Analytics 

AI-powered analytics can analyze massive datasets to identify patterns beyond human capability. Traditional diagnostic methods often rely on visible symptoms, but AI can detect subtle warning signs long before they manifest. 

For example, real-time analytics in healthcare is proving life-saving in sepsis detection. Hospitals that employ AI-driven early warning systems have reported a 20% drop in sepsis mortality rates as these systems detect irregularities in vitals and trigger timely interventions. 

2. Personalized Treatment Plans 

AI-powered analytics can customize plans for individual patients based on genetic data, medical history, and lifestyle. This shift towards precision medicine eliminates the conventional one-size-fits-all approach. 

AI also enables real-time patient monitoring and adjusting treatments based on continuous data collection from wearable devices and electronic health records (EHRs). This level of personalization is paving the way for safer, more effective treatments. 

3. Smarter Hospital Operations 

Hospitals generate 2,314 exabytes of data annually, yet much of it remains underutilized. AI-powered analytics is changing that by optimizing hospital operations to reduce inefficiencies and improve patient flow management. 

For instance, Mount Sinai Hospital in New York uses AI-powered analytics for patient care by predicting life-threatening complications before they escalate. A clinical deterioration algorithm analyzes patient data daily, identifying 15 high-risk patients for immediate intervention by an intensive care rapid response team. Beyond emergency care, AI also prevents falls, detects delirium, and identifies malnutrition risks, ensuring proactive treatment. 

4. Drug Discovery and Development 

Developing a new drug is expensive and time-consuming, often taking 10-15 years and costing over $2.6 billion. However, AI-powered analytics is significantly reducing both time and costs by analyzing millions of chemical compounds, predicting potential drug candidates, and streamlining clinical trials faster than traditional methods. 

During the COVID-19 pandemic, AI played a crucial role in identifying potential antiviral treatments by rapidly analyzing millions of drug interactions – a process that would have taken human researchers years. Additionally, AI is now being used to repurpose existing drugs, optimize trial designs, and predict patient responses, making pharmaceutical development faster, more efficient, and data-driven. 

5. 24/7 Patient Support with AI Chatbots and Virtual Assistants 

A survey by Accenture estimates that AI applications, including chatbots, could save the U.S. healthcare system around $150 billion annually by 2026. These savings stem from improved patient access and engagement, as well as a reduction in costs linked to in-person medical visits. AI-driven healthcare analytics is making healthcare more efficient, patient-centric, and responsive to individual needs. 

Challenges in AI-Driven Healthcare

Despite its potential to revolutionize healthcare, AI-powered healthcare data & analytics come with challenges that must be addressed for widespread adoption. Some of the challenges are: 

Data Privacy and Security: Healthcare systems handle sensitive patient data, making them prime targets for cyberattacks. Ensuring robust encryption, strict access controls, and compliance with HIPAA and GDPR is critical to maintaining patient trust and regulatory adherence. 

Bias in AI Models: If AI systems are trained on biased datasets, they can perpetuate healthcare disparities, thereby leading to misdiagnoses and unequal treatment recommendations. Developing diverse, high-quality datasets and regularly auditing AI models can help mitigate bias. 

Regulatory Compliance: AI-driven healthcare solutions must align with strict regulations to ensure ethical use. Organizations must work closely with regulatory bodies to maintain transparency and uphold ethical AI practices. 

What’s Next in Smart Healthcare?

AI-Powered Surgeries: Robotic assistance enhances precision and reduces risks.

Smart Wearables: Track vital signs in real-time and alert patients to anomalies.

Mental Health Tech: Predictive tools offer proactive support and personalized therapy.

Why It Matters

AI isn’t replacing doctors—it’s augmenting their decision-making with data-driven insights. Healthcare systems that adopt analytics will see:

Improved patient outcomes

Reduced costs

Streamlined operations

[chemxpertdatabase]

Elevate Your Pharma Strategy with Chemxpert’s ChemProtel

Chemxpert Database, a trusted name among pharmaceutical data providers in India, offers ChemProtel — an advanced product intelligence platform tailored for pharma professionals. Gain actionable product intelligence through our comprehensive pharma suppliers database, enabling smarter sourcing, competitive analysis, and strategic planning. Whether you're in procurement, R&D, or business development, ChemProtel by Chemxpert Database empowers you with real-time insights to stay ahead in the dynamic pharmaceutical landscape. Discover the smarter way to work with pharma data.

[ashconn]

Every Second Counts: Smarter Emergency Medical Services | ASHCONN

At ASHCONN, we know that in emergencies, every second matters. That’s why we’re dedicated to making emergency medical services more efficient. Our solutions help first responders and hospitals communicate seamlessly, ensuring patients receive timely care when it counts the most.

With real-time data sharing and automated alerts, we’re simplifying the process so that healthcare teams can focus on what really matters: saving lives. Join us in making a difference in emergency care.

Website: https://ashconn.com/

[instantedownloads]

How to Use n8n and AI to Build an Automation System

Automation is changing how we work every day. It helps save time, reduce mistakes, and get more done with less effort. If you want to automate your tasks but don’t know where to start, this guide is for you. In this post, you will learn how to use n8n — a free, open-source automation tool — combined with AI to build smart workflows that do work for you. What Is n8n? n8n (pronounced…

[nearlearn6]

Looking for a reliable platform to kickstart or advance your tech career? This infographic highlights the key reasons why Nearlearn is a trusted name in the EdTech industry. From industry-focused courses and expert trainers to hands-on learning, flexible formats, and strong placement support — Nearlearn is helping learners transform their careers with confidence.

Whether you're a student, a working professional, or someone looking to switch fields, Nearlearn offers a learning path tailored just for you.

Explore the core features that make Nearlearn a go-to destination for AI, Machine Learning, Python, and other in-demand tech skills.

Checkout the nearlearn website:https://nearlearn.com/courses/ai-and-machine-learning/machine-learning-with-python-training

[jcmarchi]

Deepfake misuse & deepfake detection (before it’s too late) - CyberTalk

New Post has been published on https://thedigitalinsider.com/deepfake-misuse-deepfake-detection-before-its-too-late-cybertalk/

Deepfake misuse & deepfake detection (before it’s too late) - CyberTalk

Micki Boland is a global cyber security warrior and evangelist with Check Point’s Office of the CTO. Micki has over 20 years in ICT, cyber security, emerging technology, and innovation. Micki’s focus is helping customers, system integrators, and service providers reduce risk through the adoption of emerging cyber security technologies. Micki is an ISC2 CISSP and holds a Master of Science in Technology Commercialization from the University of Texas at Austin, and an MBA with a global security concentration from East Carolina University.

In this dynamic and insightful interview, Check Point expert Micki Boland discusses how deepfakes are evolving, why that matters for organizations, and how organizations can take action to protect themselves. Discover on-point analyses that could reshape your decisions, improving cyber security and business outcomes. Don’t miss this.

Can you explain how deepfake technology works? 

Deepfakes involve simulated video, audio, and images to be delivered as content via online news, mobile applications, and through social media platforms. Deepfake videos are created with Generative Adversarial Networks (GAN), a type of Artificial Neural Network that uses Deep Learning to create synthetic content.

GANs sound cool, but technical. Could you break down how they operate?

GAN are a class of machine learning systems that have two neural network models; a generator and discriminator which game each other. Training data in the form of video, still images, and audio is fed to the generator, which then seeks to recreate it. The discriminator then tries to discern the training data from the recreated data produced by the generator.

The two artificial intelligence engines repeatedly game each other, getting iteratively better. The result is convincing, high quality synthetic video, images, or audio. A good example of GAN at work is NVIDIA GAN. Navigate to the website https://thispersondoesnotexist.com/ and you will see a composite image of a human face that was created by the NVIDIA GAN using faces on the internet. Refreshing the internet browser yields a new synthetic image of a human that does not exist.

What are some notable examples of deepfake tech’s misuse?

Most people are not even aware of deepfake technologies, although these have now been infamously utilized to conduct major financial fraud. Politicians have also used the technology against their political adversaries. Early in the war between Russia and Ukraine, Russia created and disseminated a deepfake video of Ukrainian President Volodymyr Zelenskyy advising Ukrainian soldiers to “lay down their arms” and surrender to Russia.

How was the crisis involving the Zelenskyy deepfake video managed?

The deepfake quality was poor and it was immediately identified as a deepfake video attributable to Russia. However, the technology is becoming so convincing and so real that soon it will be impossible for the regular human being to discern GenAI at work. And detection technologies, while have a tremendous amount of funding and support by big technology corporations, are lagging way behind.

What are some lesser-known uses of deepfake technology and what risks do they pose to organizations, if any?

Hollywood is using deepfake technologies in motion picture creation to recreate actor personas. One such example is Bruce Willis, who sold his persona to be used in movies without his acting due to his debilitating health issues. Voicefake technology (another type of deepfake) enabled an autistic college valedictorian to address her class at her graduation.

Yet, deepfakes pose a significant threat. Deepfakes are used to lure people to “click bait” for launching malware (bots, ransomware, malware), and to conduct financial fraud through CEO and CFO impersonation. More recently, deepfakes have been used by nation-state adversaries to infiltrate organizations via impersonation or fake jobs interviews over Zoom.

How are law enforcement agencies addressing the challenges posed by deepfake technology?

Europol has really been a leader in identifying GenAI and deepfake as a major issue. Europol supports the global law enforcement community in the Europol Innovation Lab, which aims to develop innovative solutions for EU Member States’ operational work. Already in Europe, there are laws against deepfake usage for non-consensual pornography and cyber criminal gangs’ use of deepfakes in financial fraud.

What should organizations consider when adopting Generative AI technologies, as these technologies have such incredible power and potential?

Every organization is seeking to adopt GenAI to help improve customer satisfaction, deliver new and innovative services, reduce administrative overhead and costs, scale rapidly, do more with less and do it more efficiently. In consideration of adopting GenAI, organizations should first understand the risks, rewards, and tradeoffs associated with adopting this technology. Additionally, organizations must be concerned with privacy and data protection, as well as potential copyright challenges.

What role do frameworks and guidelines, such as those from NIST and OWASP, play in the responsible adoption of AI technologies?

On January 26th, 2023, NIST released its forty-two page Artificial Intelligence Risk Management Framework (AI RMF 1.0) and AI Risk Management Playbook (NIST 2023). For any organization, this is a good place to start.

The primary goal of the NIST AI Risk Management Framework is to help organizations create AI-focused risk management programs, leading to the responsible development and adoption of AI platforms and systems.

The NIST AI Risk Management Framework will help any organization align organizational goals for and use cases for AI. Most importantly, this risk management framework is human centered. It includes social responsibility information, sustainability information and helps organizations closely focus on the potential or unintended consequences and impact of AI use.

Another immense help for organizations that wish to further understand risk associated with GenAI Large Language Model adoption is the OWASP Top 10 LLM Risks list. OWASP released version 1.1 on October 16th, 2023. Through this list, organizations can better understand risks such as inject and data poisoning. These risks are especially critical to know about when bringing an LLM in house.

As organizations adopt GenAI, they need a solid framework through which to assess, monitor, and identify GenAI-centric attacks. MITRE has recently introduced ATLAS, a robust framework developed specifically for artificial intelligence and aligned to the MITRE ATT&CK framework.

For more of Check Point expert Micki Boland’s insights into deepfakes, please see CyberTalk.org’s past coverage. Lastly, to receive cyber security thought leadership articles, groundbreaking research and emerging threat analyses each week, subscribe to the CyberTalk.org newsletter.

[asharid]

Grok AI: Chatbot Revolusioner dari Elon Musk yang Viral di Platform X

Kecerdasan buatan (AI) telah menjadi salah satu topik terhangat dalam beberapa tahun terakhir, mendorong inovasi dan perubahan besar di berbagai sektor. Salah satu proyek terbaru yang menarik perhatian adalah Grok AI, chatbot AI yang dikembangkan oleh xAI, perusahaan milik Elon Musk. Grok AI dengan cepat menjadi viral di platform X (sebelumnya Twitter), berkat pendekatan inovatif dan gaya…

[datapeakbyfactr]

AI-Powered Decision-Making vs. Human Expertise: Who Wins? 

Artificial intelligence is already woven into the fabric of our daily lives. Whether you're getting personalized song suggestions on Spotify, seeing curated content on Netflix, navigating traffic with Google Maps, or having your email sorted by importance in Gmail, AI is quietly and powerfully shaping the choices we make. These AI-driven tools are making decisions on our behalf every day, often without us even realizing it. 

As AI continues to evolve, its role is expanding from recommending entertainment to influencing high-stakes decisions in healthcare, finance, law enforcement, and beyond. This growing presence raises a critical question: Can AI truly make better decisions than experienced human professionals or does it still fall short in areas where human judgment and intuition reign supreme? 

Understanding the Players: AI and Human Experts 

What Is AI-Powered Decision-Making? 

AI-powered decision-making refers to the use of algorithms, often driven by machine learning, neural networks, and deep learning, to analyze large datasets and generate insights, predictions, or recommendations. These systems can learn from experience, identify patterns humans may miss, and make decisions without fatigue or bias (at least in theory). 

Key strengths include: 

Speed and scale: AI can process terabytes of data in seconds. 

Pattern recognition: It detects trends and anomalies better than humans in complex datasets. 

Consistency: AI doesn’t suffer from emotions, distractions, or exhaustion. 

What Defines Human Expertise? 

Human expertise, on the other hand, is built on years, sometimes decades, of learning, intuition, and contextual understanding. An expert blends theoretical knowledge with practical experience, social awareness, and ethical judgment. 

Human strengths include: 

Contextual understanding: Experts can interpret ambiguous or nuanced situations. 

Empathy and ethics: Humans bring emotional intelligence and moral reasoning to decisions. 

Adaptability: Experts can pivot strategies in response to changing circumstances or incomplete data. 

So, which is better? As with many complex questions, the answer depends on the context. 

When AI Outperforms Humans 

1. Data-Heavy Decisions 

AI shines when the decision-making process requires analyzing vast amounts of data quickly. In fields like finance and healthcare, AI systems are revolutionizing decision-making. 

Example: Medical diagnostics. AI algorithms trained on millions of medical images have demonstrated higher accuracy than radiologists in detecting certain cancers, such as breast and lung cancers. These systems can spot subtle patterns undetectable to the human eye and reduce diagnostic errors. 

2. Predictive Analytics 

AI’s ability to forecast outcomes based on historical data makes it incredibly powerful for strategic planning and operations. 

Example: Retail and inventory management. AI can predict which products will be in demand, when restocking is necessary, and how pricing strategies will affect sales. Amazon’s supply chain and logistics systems are powered by such predictive tools, allowing for just-in-time inventory and efficient deliveries. 

3. Repetitive, Rule-Based Tasks 

AI thrives in environments where rules are clear and outcomes can be mathematically modelled. 

Example: Autonomous vehicles. While not perfect, AI is capable of processing sensor data, mapping environments, and making real-time navigation decisions; tasks that are highly rule-based and repetitive. 

Where Human Expertise Wins 

1. Complex, Ambiguous Situations 

Humans excel in “grey areas” where rules are unclear, data is incomplete, and judgment calls must be made. 

Example: Crisis management. In rapidly evolving scenarios like natural disasters or geopolitical conflicts, experienced human leaders are better at weighing intangible factors such as public sentiment, cultural nuances, and ethical trade-offs. 

2. Empathy and Human Interaction 

Some decisions require understanding human emotions, motivations, and relationships which are areas where AI still lags significantly. 

Example: Therapy and counselling. While AI chatbots can offer basic mental health support, human therapists offer empathy, intuition, and adaptive communication that machines cannot replicate. 

3. Ethical Judgment 

Ethical dilemmas often involve values, societal norms, and moral reasoning. Human decision-makers are uniquely equipped to handle such complexity. 

Example: Autonomous weapons and warfare. Should an AI-powered drone have the authority to make life-or-death decisions? Most ethicists and governments agree that moral accountability should rest with humans, not algorithms. 

“The goal is to create AI that can collaborate with people to solve the world’s toughest problems, not replace them.”

— Demis Hassabis (CEO and Co-founder of DeepMind)

AI vs. Human in Chess and Beyond 

In 1997, IBM’s Deep Blue defeated world chess champion Garry Kasparov; a symbolic moment that marked AI’s growing capabilities. Today, AI engines like AlphaZero play chess at a superhuman level, discovering strategies that human players never imagined. 

But even Kasparov himself has advocated for “centaur chess” which is a form of play where humans and AI collaborate. He argues that human intuition, combined with machine calculation, makes for the most powerful chess strategy. 

This concept extends beyond the game board. In many domains, the ideal approach may not be AI versus humans, but AI with humans. 

Toward a Collaborative Future: The Human-AI Team

Rather than replacing humans, the most promising applications of AI lie in augmenting human decision-making. This “centaur model” or “human-in-the-loop” approach brings out the best in both.

Examples of Human-AI Collaboration: 

Healthcare: AI can screen X-rays, while doctors make the final diagnosis and communicate with patients. 

Recruitment: AI can sort resumes and highlight top candidates, but human recruiters assess cultural fit and conduct interviews. 

Customer service: AI chatbots handle routine queries, while complex issues are escalated to human agents. 

This hybrid approach ensures accuracy, empathy, and accountability, all while improving efficiency.  

Challenges & Considerations 

Even as we embrace AI, several challenges must be addressed: 

Bias in AI: If the data AI learns from is biased, its decisions will be too. Human oversight is essential to ensure fairness and ethical outcomes. 

Transparency: Many AI systems are “black boxes,” making it hard to understand how decisions are made. 

Accountability: Who is responsible when an AI system makes a wrong call? Legal and regulatory frameworks are still catching up. 

Job displacement: As AI takes over certain tasks, reskilling and transitioning the workforce become critical priorities. 

Final Verdict: Who Wins? 

The battle between AI and human expertise doesn’t have a single winner because it's not a zero-sum game. AI wins in data-heavy, rules-based, and high-speed environments. Humans excel in judgment, empathy, and moral reasoning. The true power lies in collaboration. 

As we move into the next phase of digital transformation, the organizations and societies that will thrive are those that leverage both machine precision and human wisdom. In this partnership, AI isn’t replacing us, it’s empowering us. 

So the real question isn’t "who wins?" it’s "how do we win together?" 

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